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dc.contributor.authorMiller, AC
dc.contributor.authorRohloff, P
dc.contributor.authorBlake, A
dc.contributor.authorDhaenens, E
dc.contributor.authorShaw, L
dc.contributor.authorTuiz, E
dc.contributor.authorGrandesso, F
dc.contributor.authorMontano, CM
dc.contributor.authorThomson, DR
dc.date.accessioned2021-02-23T21:41:24Z
dc.date.available2021-02-23T21:41:24Z
dc.date.issued2020-12-05
dc.date.submitted2021-01-25
dc.identifier.urihttp://hdl.handle.net/10144/619852
dc.description.abstractBackground Population-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre’s Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala. Results We successfully used Epicentre’s Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h. Conclusion In combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population–the unhoused, street dwellers or people living in vehicles.en_US
dc.language.isoenen_US
dc.publisherBMCen_US
dc.rightsWith thanks to BMC.en_US
dc.titleFeasibility of satellite image and GIS sampling for population representative surveys: a case study from rural Guatemalaen_US
dc.identifier.journalInternational Journal of Health Geographicsen_US
refterms.dateFOA2021-02-23T21:41:25Z


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